Utilizing missed prediction

ABSTRACT

A system includes a power source and a memory to store an event that is predicted to occur, an action to be performed by the power source to increase efficiency of the power source during the stored event, and a mis-prediction counter indicating a count of mis-predictions. The system further includes an ECU designed to predict that the stored event will occur and to control the power source to take the action when the stored event is predicted to occur. The ECU is further designed to update the mis-prediction counter and to adjust at least one of the stored event that is predicted to occur, the stored action to be performed by the power source, or a prediction horizon of the prediction when the mis-prediction counter reaches or exceeds a threshold quantity of mis-predictions to increase efficiency of the power source during a subsequent prediction of the stored event.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/591,730 entitled “Utilizing Missed Prediction,” filed on May 10,2017, now U.S. Pat. No. 10,252,710, which is hereby incorporated byreference herein in its entirety.

BACKGROUND 1. Field

The present disclosure relates to systems and methods for utilizingmissed vehicle predictions to enhance future predictions of events.

2. Description of the Related Art

Electronic control systems for vehicles have been increasing in quantityand improving in quality for years. Such control systems may takeactions to increase efficiency of the vehicle for various reasons, suchas to save fuel cost for the driver, reduce emissions, and the like.Some of these control systems may learn driver behaviors and may controlvarious features of the vehicle based on the learned driver behaviors.For example, a control system may learn preferred acceleration rates ofa driver, a deceleration rate of a driver, lane changing habits of adriver, or the like. After learning this information, the control systemmay predict actions the driver will take in certain situations.

Based on these predictions, the control system may take various actionsto improve vehicle operations. However, at times, these predictions maybe incorrect and may result in the control system taking an undesirableaction. Thus, there is a need for systems and methods for improvingvehicle efficiency by utilizing missed predictions.

SUMMARY

Described herein is a system for improving vehicle efficiency of avehicle. The system includes a power source designed to generate powerusable to propel the vehicle. The system also includes a memory designedto store various information. For example, the memory may store an eventthat is predicted to occur and an action to be performed by the powersource to increase efficiency of the power source during the storedevent. The memory may also store a mis-prediction counter correspondingto a quantity of times a prediction of the stored event was incorrect.The system further includes an electronic control unit (ECU) coupled tothe power source and the memory. The ECU is designed to predict that thestored event will occur. The ECU is further designed to control thepower source to take the action when the stored event is predicted tooccur. The ECU is further designed to increase the mis-predictioncounter if the prediction was inaccurate. The ECU is further designed toadjust at least one of the stored events that is predicted to occur, thestored action to be performed by the power source, or a predictionhorizon of the prediction when the mis-prediction counter reaches orexceeds a threshold quantity of mis-predictions. Such adjustment mayincrease the efficiency of the power source during a subsequentprediction of the stored event.

Also described is a method for improving vehicle efficiency of avehicle. The method includes generating, by a power source, power topropel the vehicle. The method further includes storing, in a memory, anevent that is predicted to occur, an action to be performed by the powersource to increase efficiency of the power source during the storedevent, and a mis-prediction counter corresponding to a quantity of timesa prediction of the stored event was incorrect. The method furtherincludes predicting, by an electronic control unit (ECU), that thestored event will occur. The method further includes controlling, by theECU, the power source to take the action when the stored event ispredicted to occur. The method further includes increasing, by the ECU,the mis-prediction counter if the prediction was inaccurate. The methodfurther includes adjusting, by the ECU, at least one of the stored eventthat is predicted to occur, the stored action to be performed by thepower source, or a prediction horizon of the prediction when themis-prediction counter reaches or exceeds a threshold quantity ofmis-predictions in order to increase the efficiency of the power sourceduring a subsequent prediction of the stored event.

Also described is a system for improving vehicle efficiency of avehicle. The system includes a power source designed to generate powerusable to propel the vehicle. The system further includes a sensordesigned to detect data corresponding to an environment of the sensor.The system further includes a memory designed to store variousinformation including a trigger and an event that is predicted to occurwhen the trigger occurs. The memory is further designed to store anaction to be performed by the power source to increase efficiency of thepower source during the stored event, and a mis-prediction countercorresponding to a quantity of times a prediction of the stored eventwas incorrect. The system further includes an electronic control unit(ECU) coupled to the power source, the sensor, and the memory. The ECUis designed to predict that the stored event will occur when the triggeroccurs and to control the power source to take the action when thestored event is predicted to occur. The ECU is further designed toanalyze the detected data to determine whether the detected dataindicates that the stored event is unlikely to occur and to control thepower source to take a different action than the stored action inresponse to determining that the stored event is unlikely to occur. TheECU is further designed to determine whether the prediction of thestored event was accurate by determining whether an actual event fitswithin a set of bounds defining a correct prediction of the storedevent. The ECU is further designed to increase the mis-predictioncounter if the prediction was inaccurate. The ECU is further designed toadjust at least one of the stored event that is predicted to occur, thestored action to be performed by the power source, or a predictionhorizon of the prediction when the mis-prediction counter reaches orexceeds a threshold quantity of mis-predictions in order to increase theefficiency of the power source after a subsequent detection of thetrigger.

BRIEF DESCRIPTION OF THE DRAWINGS

Other systems, methods, features, and advantages of the presentinvention will be or will become apparent to one of ordinary skill inthe art upon examination of the following figures and detaileddescription. It is intended that all such additional systems, methods,features, and advantages be included within this description, be withinthe scope of the present invention, and be protected by the accompanyingclaims. Component parts shown in the drawings are not necessarily toscale, and may be exaggerated to better illustrate the importantfeatures of the present invention. In the drawings, like referencenumerals designate like parts throughout the different views, wherein:

FIG. 1 is a block diagram illustrating various components of a vehiclethat includes a system for increasing efficiency of the vehicle based onmissed predictions according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating a method for increasing efficiency ofa vehicle by utilizing missed predictions according to an embodiment ofthe present invention;

FIGS. 3A and 3B are flowcharts illustrating a method for increasingefficiency of a vehicle by utilizing missed predictions and bysupplementing the predictions with additional data detected by varioussensors of the vehicle according to an embodiment of the presentinvention;

FIG. 4 is a drawing illustrating an exemplary database for storingvarious information usable by a vehicle to increase efficiency based onmissed predictions according to an embodiment of the present invention;and

FIG. 5 is a drawing of a vehicle on a road utilizing the informationstored in the database of FIG. 4 in order to increase vehicle efficiencyaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

The present disclosure describes systems and methods for utilizingmissed predictions to increase efficiency of vehicles. An exemplarysystem includes a power source, such as an engine and/or a motorgenerator. The system also includes a memory that stores informationsuch as predicted events, actions to be taken by the power source whenthe event is predicted to occur, and mis-prediction counters indicatinga number of times a corresponding prediction was incorrect. The systemalso includes an electronic control unit (ECU). The ECU may monitor thestatus of the vehicle and predict when a stored event will occur. Whenthe ECU predicts that the stored event will occur, the ECU will controlthe power source to take the corresponding action stored in the memory.The ECU will further supplement the mis-prediction counter each time thecorresponding prediction is incorrect. When the mis-prediction counterreaches a threshold quantity of mis-predictions, the ECU may adjustfuture predictions of the event, may adjust the stored action to betaken by the power source, or may adjust a prediction horizon of theprediction.

Such a system provides benefits and advantages such as utilizing missedpredictions to increase future efficiency of the vehicle. Because theECU keeps count of the missed predictions, the ECU may determinerelatively quickly whether a prediction is invalid. This provides thebenefit of predictions having a greater accuracy, as well as the benefitof allowing the vehicle to operate with increased efficiency because ofthe increased prediction accuracy. The ECU may store data indicatingtimes and/or locations at which mis-predictions occur, advantageouslyallowing for more accurate predictions that are based on particulartimes and/or locations. This further increases vehicle efficiency. Thesystem also detects data using various sensors and uses the detecteddata to determine whether a prediction is likely to be incorrect. Thisadvantageously allows a prediction to be changed for any given instancein which the prediction is likely to be incorrect, allowing the vehicleto be controlled to have increased efficiency for the particularinstance. These benefits may further be enhanced if the vehicle is anautonomous vehicle because autonomous vehicles may make more predictionsthan human-driven vehicles.

The benefits described above may apply equally to hybrid vehicles,conventional vehicles, electric vehicles, fuel cell vehicles, and thelike. For example, operation of a hybrid vehicle may be enhanced bydetermining and taking action in regards to mis-predictions regardingcharging of the battery and when to turn on or off the engine. In asimilar manner, operation of a conventional vehicle may be enhanced bydetermining and taking action in regards to mis-predictions regardingshifting of the transmission. An electric vehicle may be enhanced bydetermining and taking action in regards to mis-predictions regardingboost voltage control.

Turning to FIG. 1, a vehicle 100 includes components of a system 101 forimproving efficiency of the vehicle 100. The vehicle 100 includes anelectronic control unit (ECU) 102, a memory 104, a global positioningsystem (GPS) sensor 106, an inertial measurement unit (IMU) sensor 108,and a network access device 110. The vehicle 100 further includes apower source which may include one or more of an engine 112 or acombination of a battery 114 and a motor generator 116. The vehicle 100may further include a transmission 118 for applying mechanical energyfrom the engine 112 or the motor generator 116 to wheels to propel thevehicle 100. The vehicle 100 further includes one or more sensorincluding a camera 120, a radio detection and ranging (radar) sensor122, a light imaging, detection, and ranging (LIDAR) sensor 124, and aweather sensor 126.

The ECU 102 may include one or more processors or controllers, which maybe specifically designed for automotive systems. The functions of theECU 102 can be implemented in a single ECU or in multiple ECUs. The ECU102 may receive data from components of the vehicle 100, may makedeterminations based on the received data, and may control theoperations of the components based on the determinations.

The memory 104 may include any non-transitory memory known in the art.In that regard, the memory 104 may store machine-readable instructionsusable by the ECU 102 and may store other data as requested by the ECU102.

The GPS sensor 106 may be capable of detecting location datacorresponding to a location of the vehicle 100. The IMU sensor 108 maydetect a velocity or an orientation of the vehicle 100. One or both ofthe GPS sensor 106 or the IMU sensor 108 may be referred to as alocation sensor and may be used to determine a current location,heading, and/or orientation of the vehicle 100.

The network access device 110 may include any port or device capable ofcommunicating via a wired or wireless interface such as Wi-Fi,Bluetooth, a cellular protocol, vehicle to vehicle communications, orthe like. For example, the ECU 102 may control the network access device110 to communicate with a cloud 128, an external vehicle(s) 130, or anyother object or device. For example, the network access device 110 mayretrieve traffic or weather information from the cloud 128. As anotherexample, the network access device 110 may retrieve data from nearbyvehicles 130 such as a speed of the vehicles 130, a quantity of thevehicles 130, a location of the vehicles 130, or the like.

The engine 112 may convert a fuel into mechanical power. In that regard,the engine 112 may be a gasoline engine, a diesel engine, a fuel cellengine, or the like.

The battery 114 may store electrical energy. The motor generator 116 mayconvert the electrical energy stored in the battery into mechanicalpower usable by the transmission 118. The motor generator 116 mayfurther convert mechanical power received from the transmission 118 toelectrical energy, which may be stored in the battery 114.

The transmission 118 may be coupled to the engine 112 and the motorgenerator 116. The transmission 118 may include a power splitter and maytransfer mechanical power received from one or both of the engine 112and the motor generator 116 to wheels of the vehicle 100. Thetransmission 118 may control how much mechanical power is transferredfrom each of the engine 112 and the motor generator 116. For example,the ECU 102 may control the transmission 118 to achieve a desired powertransfer from each of the engine 112 and the motor generator 116. Thetransmission 118 may further transfer mechanical energy received fromone or both of the engine 112 or wheels of the vehicle 100 to the motorgenerator 116 for conversion into electrical energy.

The camera 120 may include one or more camera oriented in such a manneras to be able to detect image data corresponding to an environment ofthe vehicle 100. For example, the camera 120 may include a camerapositioned on each end of the vehicle 100 to detect the presence ofobjects in the environment of the vehicle 100.

The radar sensor 120 may include one or more radar device oriented insuch a manner as to be able to detect radar data corresponding to anenvironment of the vehicle 100. For example, the radar sensor 120 maytransmit a radar beam, receive a reflection of the radar beam, andanalyze the reflection of the radar beam to determine the presence andcharacteristics of an object in the environment of the vehicle 100.

The LIDAR sensor 124 may include one or more LIDAR device oriented insuch a manner as to be able to detect LIDAR data corresponding to anenvironment of the vehicle 100. For example, the LIDAR sensor 124 maytransmit light, receive a reflection of the light, and analyze thereflection of the light to determine the presence and characteristics ofan object in the environment of the vehicle 100. Inclusion of the radarsensor 122 and the LIDAR sensor 124 may be advantageous as the radarsensor 122 may be better suited to detect data in some environmentalconditions, while the LIDAR sensor 124 may be better suited to detectdata in other environmental conditions.

The weather sensor 126 may include any sensor capable of detecting datacorresponding to weather conditions outside of the vehicle 100. Forexample, the weather sensor 126 may detect an amount of ambient light,may detect an ambient temperature of the environment, may detectmoisture in the environment, may detect the presence of clouds, or thelike.

In order to control the vehicle 100 to perform in an efficient manner,the ECU 102 may make predictions regarding use of the vehicle in certainsituations and may control one or more of the engine 112, the battery114, the motor generator 116, or the transmission 118 based on theprediction. For example, the ECU 102 may predict that a driver of thevehicle 100 will request that the vehicle 100 be accelerated to 65 milesper hour (mph) while on a highway on-ramp. In that regard, theprediction may be triggered when the vehicle 100 is approaching anon-ramp. As the ECU 102 determines that the vehicle 100 is approachingthe on-ramp, the ECU 100 may control the power source to prepare forsuch acceleration. Such preparation may be performed in order to improveor enhance efficiency of the power source.

The engine 112 may perform most efficiently when operating at apredetermined engine speed and torque. In that regard, when the ECU 102predicts that the vehicle will accelerate to 65 mph, the ECU 102 maycontrol the engine 112 to turn on and operate at the predeterminedengine speed and torque before the predicted acceleration begins. Suchoperation of the engine 112 may provide more power than necessary forthe present operation, and the extra power may be used to charge thebattery 114. Accordingly, when the acceleration begins, the engine 112may continue to operate at the predetermined engine speed and torque andany additional power may be provided by the motor generator 116 usingenergy stored in the battery 114. Because the engine 112 is continuouslyoperating at the most efficient engine speed and torque, the totalefficiency of the vehicle 100 is improved.

Although taking actions in response to predicting upcoming events (suchas in the example described above) may increase efficiency of thevehicle 100 (i.e., increased fuel efficiency, efficient use ofelectrical power, or the like), the efficiency of the vehicle may bereduced when a mis-prediction occurs. A mis-prediction may be aninstance in which a predicted event does not occur, a predicted eventoccurs with different parameters (i.e., the vehicle 100 accelerates aspredicted but at a different rate than predicted), a predicted eventoccurs but the action taken in response to predicting the event resultsin decreased fuel efficiency, or the like.

The ECU 102 may be designed to account for such mis-predictions. Forexample, the ECU 102 may count mis-predictions and determine theseverity of mis-predictions for various predicted events. If a certainquantity of mis-predictions has occurred (and/or sufficiently severemis-predictions have occurred) then the ECU 102 may take action such aschanging a control associated with the prediction.

Furthermore, data detected by one or more sensor of the vehicle 100 mayindicate that a predicted event is unlikely to occur. The ECU 102 mayreceive the detected data and may take an action other than a storedaction that is associated with the predicted event.

Referring to FIG. 2, a method 200 for accounting for mis-predictions ordetection of data indicating that a prediction may be incorrect isshown. For learning and prediction, calculations and memory may berelatively expensive due to storing information for a wide range ofscenarios. To alleviate this issue, averages and reduced categorizationsmay be used. Predictions may also utilize a time window, or “predictionhorizon,” for determining future controls. At the beginning of aprediction event, the future controls may be relatively locked in inorder to gain optimal efficiency. Utilizing the method 200, the vehiclemay keep the original control information in order to minimizecalculations and data, but may store an additional relatively smallsubset of information related to learning mis-predictions.

In block 202, an ECU of a vehicle may make a relatively long termprediction corresponding to an event of the vehicle. For example, theECU may make associations between triggers and events as the vehicle isdriven. After a certain number of associations have been determined, theECU may begin to make predictions based on the associations. Forexample, if the vehicle accelerates to 65 miles per hour (mph) each timethe vehicle reaches a highway on-ramp and this event has occurred acertain number of times (such as 20, 50, 100, or the like) then the ECUmay predict that the vehicle will accelerate to 65 mph when the vehiclereaches a highway on-ramp. The ECU may store the trigger (reaching theon-ramp), the predicted event (accelerate to 65 mph), and the action tobe performed.

As the vehicle continues to be driven in block 204, the ECU may counthow many mis-predictions have occurred and may determine the severity ofthe mis-predictions. Continuing the example, each time the vehiclereaches an on-ramp and fails to accelerate to 65 mph, the ECU mayincrease a mis-prediction counter. Furthermore, each time the vehiclefails to accelerate to 65 mph, the ECU may determine to which speed thevehicle accelerates and how far the actual speed is from the predicted65 mph.

In block 206, the ECU may determine whether the mis-prediction counterreaches or exceeds a threshold quantity of mis-predictions. Thethreshold quantity of mis-predictions corresponds to a quantity ofmis-predictions at which a prediction may be considered to be unreliablefor a given situation. The ECU may further determine whether thedifference between the prediction and an actual event is greater than apredetermined threshold. The predetermined threshold corresponds to adifference between a predicted event and an actual event that issufficiently great to indicate that the prediction may be considered tobe unreliable. The predetermined threshold may also correspond to adifference that is sufficiently great to indicate that implementation ofthe stored action will result in relatively large and efficiency.

In block 208, the ECU may receive information that is related to themis-prediction. For example, the ECU may receive information such ascurrent weather conditions, current traffic conditions, a current timeof day, a current location, or the like. This information may bereceived from a sensor of the vehicle, from the cloud, from one or morenearby vehicles, or the like.

In block 210, the ECU may change a control with regard to the predictedevent or the action to be performed when the event is predicted. Thechange of control may be referred to as a short term change of controlbecause the change may occur in a shorter time frame than the ECU takesto make a long-term prediction such as those in block 202. For example,if 50 iterations are required for the long-term prediction of block 202to take effect, the change in block 210 may occur after 5 iterations, 10iterations, or the like.

The change of control may include changing the predicted event that isassociated with a given trigger, changing an action that is to beperformed when the event is predicted, or changing a prediction horizonof the prediction. These control changes will be discussed in moredetail below.

Referring to FIGS. 3A and 3B, a method 300 for improving efficiency of avehicle, such as the vehicle 100 of FIG. 1, is shown. The method 300 maybe performed by components of a vehicle, such as the vehicle 100 of FIG.1.

In block 302, a power source of the vehicle may generate power to propelthe vehicle in a forward or aft direction. The power source may includean engine, a fuel cell, a motor generator with a battery, or the like.In some embodiments, the vehicle may be a hybrid vehicle such that thepower source includes an engine and a combination of a battery and amotor generator.

Certain information may be stored in a memory of the vehicle as shown inblock 304. For example, the memory may store a plurality of triggersthat may be used to predict an upcoming event. Triggers may include, forexample, locations, times, requested accelerations, braking actions, orthe like.

The memory may also store events that are predicted to occur in responseto the triggers. The predicted events may be based on learned behaviorsof an individual driver, information referenced from a database or a setof lookup tables, and/or other information. An event may include, forexample, an acceleration rate, a braking action, a next segment of aroute, a certain vehicle speed, an efficiency of the power source, orthe like.

The triggers and the events may be associated in the memory such that anevent is predicted to occur when the trigger is detected. For example, atrigger may include an indication that the vehicle is approaching a stopsign and the corresponding event may be that the vehicle decelerates toa stop at the stop sign.

The memory may also store an action to be performed by the power sourceto increase efficiency in preparation of or during the event. The actionmay include any action performable by the power source. For example, theaction may include turning an engine of a hybrid vehicle on, causing abattery of a vehicle to charge to a predetermined state of charge (SOC),causing a braking operation to begin earlier than requested to optimizeresults of regenerative braking, or the like.

Continuing the above example, the ECU may predict that the vehicle willdecelerate to a stop at the stop sign and may cause the vehicle to beginbraking before braking is requested by the driver. Such an action mayincrease an amount of electrical energy that the motor generator mayprovide to the battery. As another example, the ECU may control theengine to increase engine speed, without starting the engine, to shortena ramp-up period for an anticipated acceleration.

The memory may further store at least one of changes to the storedactions or backup options to be implemented in the case that themis-prediction counter reaches the predetermined quantity or that thepredicted events is relatively far from an actual event. In that regard,the information stored in the memory may be used by the ECU to take adifferent action if the mis-prediction counter reaches the predeterminedquantity or if the actual event is sufficiently far from the predictedevent.

The memory may store the mis-prediction counter corresponding to aquantity of times a prediction has been inaccurate. The memory may alsostore additional data corresponding to mis-predictions. For example, thememory may store differences between predicted events and actual event,specific times at which the mis-predictions occurred, specific locationsat which the mis-predictions occurred, and the like.

The ECU may continually monitor for triggers. For example, the ECU maymonitor data detected by various sensors of the vehicle for detection oftriggers. For example, the ECU may monitor location data from a GPSsensor, image data from a camera, radar data from a radar sensor, or thelike. In block 306, when a trigger is detected, the ECU may predict thatthe stored event that is associated with the trigger will occur. Forexample, the ECU may receive image data corresponding to a stop signfrom the camera and may predict that the vehicle will decelerate to astop by the time the vehicle reaches the stop sign.

In response to predicting that the event will occur, the ECU may controlthe power source of the vehicle to take the stored action that isassociated with the predicted event. For example, the ECU may controlthe motor generator to begin regenerative braking to convert mechanicalpower into electrical energy before such braking is requested by thedriver.

As mentioned above, certain data detected by the vehicle may indicatethat a predicted event is unlikely to occur. For example, conditions maychange suddenly when driving, which may make an original predictionunlikely. In that regard and in block 310, the sensors of the vehiclemay continuously or periodically detect data corresponding to theenvironment of the vehicle. For example, the sensors may detect datasuch as the presence of objects near the vehicle, current weatherconditions such as sunshine or moisture, traffic, road construction, orthe like.

In some embodiments, the network access device may also or insteadreceive data from at least one of the cloud or nearby vehiclescorresponding to the environment of the vehicle. For example, thenetwork access device may receive weather information from the cloud,may receive data indicating the presence or vehicle speed of nearbyvehicles, or the like.

In block 312, the ECU may continuously or periodically analyze the datadetected from the various sensors of the vehicle and/or the datareceived by the network access device. The ECU may analyze the data todetermine if any of the data indicates that the predicted event isunlikely to occur.

Various types of data may indicate that various events are unlikely tooccur. For example, a traction sensor of the vehicle may detect dataindicating that the current traction is relatively low, indicating thata relatively high rate of acceleration is unlikely. As another example,the camera may detect the presence of an object in front of the vehicle,indicating that an acceleration is unlikely and, potentially, that adeceleration is likely. As yet another example, the network accessdevice may receive data from the cloud indicating that vehicles on ahighway are traveling at a relatively low speed, indicating that thevehicle may not accelerate to the speed limit of the highway while on anon-ramp to the highway.

In block 314, the ECU may control the power source to take an actionother than the stored action when data indicates that the predictedevent is unlikely to occur. In some embodiments, the new action taken bythe ECU may include taking no action other than that requested by adriver. In some embodiments, the new action may be to cancel the storedaction. In some embodiments, the new action may be to delay theprediction. In some embodiments, the ECU may determine a new action totake based on the specifics of the detected data. In some embodiments,the ECU may implement a backup stored action associated with thepredicted event.

As an example, the ECU may predict that the vehicle will accelerate to65 mph while traveling along an on-ramp to a highway. The stored actionassociated with this prediction is for the ECU to control the powersource to begin accelerating prior to reaching the on-ramp in order toincrease efficiency by not operating the engine at an engine speed thatis greater than an optimal engine speed. The ECU may receive data from anearby vehicle indicating that the current rate of speed of vehicles onthe highway is 45 mph. In that regard, the ECU may implement a newaction that delays the acceleration by a certain amount of time so thatthe engine can accelerate at the optimal engine speed and be travelingat 45 mph at the end of the on-ramp.

As another example, a vehicle may predict an acceleration andpreemptively increase engine speed to support such acceleration.However, a sensor of the vehicle may detect a change in condition, suchas an animal darting into the lane of the vehicle. The ECU may, at thatpoint, stop increasing the engine speed. When the animal is out of thepath of the vehicle, the vehicle may resume the previously predictedacceleration, as originally determined. Alternatively, the vehicle maydetermine a new acceleration prediction based on any changed conditionsand may take an action to preemptively facilitate the newly predictedacceleration.

In block 316, the ECU may determine whether the predicted action wascorrect. The predicted action may be associated with a set of boundsthat define a correct prediction of the stored event. For example, thepredicted action may be for the vehicle to accelerate to 65 mph on anon-ramp to a highway. The bounds may be stored in the memory ordetermined by the ECU. For example, the ECU may determine that any finalspeed within 10% of 65 mph corresponds to a correct prediction. Asanother example, the memory may store data indicating that a final speedbetween 60 mph and 70 mph corresponds to a correct prediction.

In some embodiments, the ECU may change the determined or stored boundsbased on collected data. Continuing the above example, if the ECUdetermines that multiple mis-predictions have occurred that eachcorrespond to final speeds of 59 mph and 71 mph, the ECU may adjust thebounds to include these values. In that regard, the bounds may be from59 mph to 71 mph, from 58 mph to 72 mph, or the like.

In block 318, the ECU may increase the mis-prediction counter if theprediction was inaccurate. The mis-prediction counter may correspond toa particular predicted event, a group of predicted events, or totalpredictions by the ECU.

In some embodiments, multiple mis-prediction counters may correspond toa single predicted event and be distinguished by location or time. Forexample, a predicted action may include that the vehicle will decelerateto a stop when approaching a stop sign. A different mis-predictioncounter may be assigned to each particular stop sign location, or adifferent mis-prediction counter may be assigned to various timesthroughout the day. In that regard, the ECU may increase amis-prediction counter corresponding to the stop sign at location A if amis-prediction occurs at that location and may not increase amis-prediction counter corresponding to stop signs at other locations.

In block 320, the ECU may determine a difference between the predictedevent and an actual occurrence of the event. The ECU may furtherdetermine whether the difference is greater than a threshold difference.The threshold difference may correspond to a difference between thepredicted event and the actual event that is sufficiently great towarrant reconsideration of the prediction or the action to be taken inresponse to the predicted event.

For example, the predicted event may be an acceleration to 65 mph on ahighway on-ramp. The bounds that define a correct prediction may be from60 mph to 70 mph. The memory may store, or the ECU may determine, athreshold difference from the predicted event. The threshold differencemay be 20 mph. In that regard, if the ECU determines that the actualevent is an acceleration to 86 mph then the ECU may determine that thedifference is greater than the threshold difference.

In some embodiments, the threshold difference may correspond to adifference between the bounds defining the correct prediction and theactual event. In that regard, the ECU may determine that the differenceexceeds the threshold difference when the actual event is anacceleration to 91 mph.

In some embodiments, the threshold difference may be set based on aquantifiable justification. For example, the threshold difference maycorrespond to values that will reduce fuel consumption of the vehicle bya predetermined amount, such as by 1 mile per gallon (mpg). In thatregard, the ECU may determine what difference in acceleration willcorrespond to a loss of 1 mpg and may set that difference as thethreshold difference. In some embodiments, a threshold difference may beset based on other considerations such as a difference in cost betweenthe prediction and the actual event, a difference in emissions betweenthe prediction and the actual event, or the like.

As another example, the threshold may correspond to a difference in timebetween a predicted duration of an event and an actual duration of anevent. For example, if the predicted event is an acceleration from 35mph to 70 mph over 25 seconds, the threshold difference may correspondto a 2 second difference. In that regard, the threshold difference maybe considered to be exceeded if the actual acceleration from 35 mph to70 mph occurs over 28 seconds.

In block 322, the ECU may take an action when at least one of themis-prediction counter reaches a threshold quantity, or the differencebetween the predicted event and the actual event is greater than thethreshold difference. In some embodiments, multiple mis-predictioncounters may correspond to the predicted event such that eachmis-prediction counter corresponds to a different severity ofmis-predictions. For example, a first mis-prediction counter maycorrespond to mis-predictions that are within 20% of the predicted eventand a second mis-prediction counter may correspond to mis-predictionsthat are greater than 20% away from the predicted event. The thresholdquantity of mis-predictions may be different for each mis-predictioncounter. For example, the threshold quantity of mis-predictions formis-predictions within 20% of the predicted event may be 10, and thethreshold quantity of mis-predictions for mis-predictions farther than20% away from the predicted event may be 3 due to the relative severityof these mis-predictions.

The actions taken by the ECU may vary based on the predicted event, theseverity of the mis-prediction, the frequency of mis-predictions, whattype of mis-prediction occurred, or the like. The actions may includeadjusting the stored event that is predicted to occur, adjusting thestored action to be performed in response to the prediction, oradjusting a prediction horizon of the prediction.

Adjusting the stored event includes changing the prediction of what willoccur when the corresponding trigger is detected. For example, thepredicted event may include accelerating to 65 mph when the vehicleapproaches a highway on-ramp. After several iterations of the vehicleaccelerating to 75 mph when approaching the on-ramp, the mis-predictioncounter may reach the threshold quantity.

When the mis-prediction counter reaches the threshold quantity, the ECUmay change the predicted event corresponding to the trigger ofapproaching the on-ramp. For example and potentially depending on aquantity of correct predictions, the ECU may change the predicted eventto be an acceleration to a speed that is between 65 mph and 75 mph.

Adjusting the stored action includes adjusting the action to beperformed when the particular event is predicted to occur. Adjusting thestored action may further include canceling the stored action.Continuing the above example, the action to be taken in response topredicting the acceleration to 65 mph may include turning on an engineearly to charge a battery with sufficient power to supplement the enginepower during the acceleration. After the mis-prediction counter reachesthe threshold quantity, the ECU may change this action. For example,because the actual final speeds are greater than the predicted finalspeed, the ECU may control the engine to turn on even earlier in orderto provide extra charge to the battery prior to the acceleration.

Adjusting the prediction horizon includes shortening or lengthening anamount of time to which a prediction applies. For example, the predictedevent may include the vehicle coasting at 65 mph upon reaching ahighway. The prediction may apply to the entire time the vehicle is on ahighway. After numerous iterations, the mis-prediction counter may reachthe threshold quantity due to the vehicle traveling at 75 mph on aparticular stretch of the highway. The ECU may change the predictionhorizon of the prediction to correspond to only the portion of thehighway at which the vehicle has traveled at 65 mph. The ECU may thencreate a new trigger corresponding to the location at which the vehicletravels at 75 mph, and may create a new prediction that the vehicle willtravel at 75 mph on the corresponding stretch of highway.

In block 324, the ECU may analyze mis-predictions to determine whether apredetermined quantity of mis-predictions occur during a particular timeof day, a particular day of the week, a particular location, or otheritem that may affect normal control of the vehicle such as snowyconditions or clear conditions. The predetermined quantity ofmis-predictions at the time, day, or location corresponds to a quantityat which it is obvious that the prediction is affected by the particulartime, day, or location.

For example, for each mis-prediction, the memory may store data in thememory indicating a time and/or a location at which the mis-predictionoccurred. Continuing the above example, the memory may store thelocation at which the vehicle begins traveling at 75 mph. As anotherexample, the ECU may note times at which a vehicle accelerates to aspeed below 65 mph while traveling along a highway on-ramp, likelycorresponding to rush hours.

In block 326, the ECU may adjust at least one of the stored event, thestored action, or a prediction horizon when the predetermined quantityof mis-predictions occurs at the time, the day, or the location. Theseadjustments may be performed in a similar manner as described above withreference to block 322.

For example, the vehicle may accelerate to 45 mph while traveling alonga highway on-ramp at times between 5 PM and 6 PM. After thepredetermined quantity of mis-predictions occurs at this time, the ECUmay change the predicted event to correspond to times other than between5 PM and 6 PM. The ECU may further create a new predicted eventcorresponding to the highway on-ramp at times between 5 PM and 6 PM. TheECU may create an action to be performed that corresponds to the sloweracceleration.

Turning to FIG. 4, an exemplary database 400 illustrates data that maybe stored by a memory of a vehicle, such as the memory 104 of FIG. 1. Asshown, the memory may store a plurality of triggers, a plurality ofpredicted events corresponding to the trigger, actions to be performedin response to predicting the event, along with backup actions. Thememory may further store a mis-prediction counter for each of thetriggers, and may store additional information corresponding to thetimes or locations at which the mis-predictions occur.

Referring to FIGS. 4 and 5, an exemplary use of the database 400 isshown. A vehicle 501 having similar features as the vehicle 100 of FIG.1 is traveling along a country road 500 and approaching an on-ramp 504to the 405 highway 502. As shown in the database 400, the memory has atrigger stored of approaching the 405 on-ramp 504.

The predicted event corresponding to the example shown in FIG. 5 is thatthe vehicle will accelerate to 65 mph over the quarter-mile distance ofthe on-ramp 504. The memory further stores a corresponding action to beperformed when the event is predicted. In particular, the action is thatthe ECU will turn on the engine early (at a location 506) and cause itto run at an optimal engine speed and torque, using the extra powerinitially to charge the battery. The purpose of this action is to allowthe engine to continue to run at the optimal settings whilesupplementing the power for the acceleration with energy stored in thebattery.

As the vehicle 501 reaches the location 506, the ECU may control theengine to turn on and operate at the optimal engine speed and torque. Asthe vehicle 501 enters the highway 502, the ECU may determine whetherthe actual vehicle speed is within the bounds that define the predictedevent. For example, the ECU may determine whether the actual vehiclespeed is between 60 mph and 70 mph. If the actual vehicle speed is lessthan 60 mph or greater than 70 mph, the ECU may increase themis-prediction counter such that the mis-prediction counter for thecorresponding event increases to 6 mis-predictions. Furthermore, the ECUmay store data corresponding to the time at which the mis-predictionoccurred. The ECU may also store information corresponding to theseverity of the mis-prediction, and whether the mis-prediction was toolow or too high.

When the mis-prediction counter reaches the threshold quantity ofmis-predictions, the ECU may cause the backup action to occur upon asubsequent prediction of the event. For example, as the vehicle 501approaches the on-ramp 504 during a subsequent trip, the ECU may allowthe engine of the vehicle 501 to remain off until acceleration isrequested by the driver.

Where used throughout the specification and the claims, “at least one ofA or B” includes “A” only, “B” only, or “A and B.” Exemplary embodimentsof the methods/systems have been disclosed in an illustrative style.Accordingly, the terminology employed throughout should be read in anon-limiting manner. Although minor modifications to the teachingsherein will occur to those well versed in the art, it shall beunderstood that what is intended to be circumscribed within the scope ofthe patent warranted hereon are all such embodiments that reasonablyfall within the scope of the advancement to the art hereby contributed,and that that scope shall not be restricted, except in light of theappended claims and their equivalents.

What is claimed is:
 1. A system for improving vehicle efficiency of avehicle comprising: a power source configured to generate power usableto propel the vehicle; a memory configured to store an event that ispredicted to occur and an action to be performed by the power source toincrease efficiency of the power source during the stored event; and anelectronic control unit (ECU) coupled to the power source and the memoryand configured to: make a prediction that the stored event will occur,control the power source to take the action when the stored event ispredicted to occur, determine a misprediction in response to theprediction being inaccurate, and adjust at least one of the stored eventthat is predicted to occur, the stored action to be performed by thepower source, or a prediction horizon of the prediction when at leastone of a sufficient quantity of mispredictions have occurred or when adifference between the stored event and an actual event is greater thana predetermined prediction threshold.
 2. The system of claim 1 whereinthe ECU is further configured to determine the misprediction bydetermining that the actual event fails to fit within a set of boundsdefining a correct prediction of the stored event.
 3. The system ofclaim 2 wherein the memory is further configured to store amisprediction counter, and the ECU is further configured to increase themisprediction counter in response to determining the misprediction. 4.The system of claim 1 further comprising a sensor configured to detectdata corresponding to an environment of the sensor, wherein the ECU isfurther configured to: determine that the stored event is unlikely tooccur based on the detected data, and control the power source to take adifferent action than the stored action in response to determining thatthe stored event is unlikely to occur.
 5. The system of claim 4 wherein:the stored event is an acceleration rate; the sensor includes at leastone of a camera, a radar, or a Light Detection and Ranging (LIDAR)configured to detect a presence of an object in a path of the sensor;and the different action includes at least one of preparing the powersource to provide less than the stored acceleration rate or causing thepower source to delay acceleration based on detection of the presence ofthe object.
 6. The system of claim 1 further comprising: a batteryhaving a state of charge (SOC) and configured to store electricalenergy; a motor coupled to the battery and configured to convert theelectrical energy into mechanical power; and an engine having an optimaloperating torque and engine speed corresponding to an optimal efficiencyof the engine and configured to convert fuel into mechanical power,wherein the stored event is an acceleration rate, and the action is tocause the engine to operate at the optimal operating torque and enginespeed prior to the acceleration rate being requested in order toincrease the SOC of the battery so that the motor and the engine canprovide sufficient mechanical power to provide the acceleration ratewith the engine operating within the optimal operating torque and enginespeed.
 7. The system of claim 1 wherein the stored event is at least oneof an acceleration rate over a predetermined distance or a predeterminedamount of time, a velocity of the vehicle after the predetermineddistance or amount of time, or the efficiency of the power source overthe predetermined distance or amount of time.
 8. The system of claim 1wherein the memory is further configured to store a backup action to beperformed by the power source to increase the efficiency of the powersource during the stored event, and the ECU is further configured tocontrol the power source to take the backup action when the at least oneof the sufficient quantity of mispredictions have occurred or when thedifference between the stored event and the actual event is greater thanthe predetermined prediction threshold.
 9. The system of claim 1 whereinthe ECU is further configured to determine whether a predeterminedquantity of mis-predictions occur during a particular time of day or aparticular location and to adjust at least one of the stored event thatis predicted to occur, the stored action to be performed by the powersource, or the prediction horizon of the prediction for only theparticular time of day or the particular location when the predeterminedquantity of mis-predictions occur during the particular time of day orat the particular location.
 10. A method for improving vehicleefficiency of a vehicle comprising: generating, by a power source, powerto propel the vehicle; storing, in a memory, an event that is predictedto occur and an action to be performed by the power source to increaseefficiency of the power source during the stored event; making aprediction, by an electronic control unit (ECU), that the stored eventwill occur; controlling, by the ECU, the power source to take the actionwhen the stored event is predicted to occur; determining, by the ECU, amisprediction in response to the prediction being inaccurate; andadjusting, by the ECU, present or future control of the power source inresponse to at least one of a sufficient quantity of mispredictions haveoccurred or when a difference between the stored event and an actualevent is greater than a predetermine prediction threshold.
 11. Themethod of claim 10 further comprising determining, by the ECU, that thedifference between the stored event and the actual event is greater thana predetermined prediction threshold when the actual event is furtherfrom a set of bounds defining a correct prediction by at least athreshold difference.
 12. The method of claim 10 further comprising:detecting, by a sensor, data corresponding to an environment of thesensor; determining, by the ECU, that the stored event is unlikely tooccur based on the detected data; and controlling, by the ECU, the powersource to take a different action than the stored action in response todetermining that the stored event is unlikely to occur.
 13. The methodof claim 12 wherein: the stored event is an acceleration rate; thesensor includes at least one of a camera, a radar, or a Light Detectionand Ranging (LIDAR) configured to detect a presence of an object in apath of the sensor; and controlling the power source to take thedifferent action includes at least one of preparing the power source toprovide less than the stored acceleration rate or causing the powersource to delay acceleration based on detection of the presence of theobject.
 14. The method of claim 10 further comprising: storing, in abattery having a state of charge (SOC), electrical energy; converting,by a motor, the electrical energy into mechanical power; and converting,by an engine having an optimal operating torque and engine speedcorresponding to an optimal efficiency, fuel into mechanical power,wherein: the stored event is an acceleration rate, and the action is tocause the engine to operate at the optimal operating torque and enginespeed prior to the acceleration rate being requested in order toincrease the SOC of the battery so that the motor and the engine canprovide sufficient mechanical power to provide the acceleration ratewith the engine operating within the optimal operating torque and enginespeed.
 15. The method of claim 10 wherein the stored event is at leastone of an acceleration rate over a predetermined distance or amount oftime, a velocity of the vehicle after the predetermined distance oramount of time, or the efficiency of the power source over thepredetermined distance or amount of time.
 16. The method of claim 10further comprising: storing, by the memory, a backup action to beperformed by the power source to increase the efficiency of the powersource during the stored event; and controlling, by the ECU, the powersource to take the backup action in response to the at least one of thesufficient quantity of mispredictions have occurred or the differencebetween the stored event and the actual event is greater than thepredetermine prediction threshold.
 17. The method of claim 10 furthercomprising: determining, by the ECU, whether a predetermined quantity ofmis-predictions occur during a particular time of day or a particularlocation; and adjusting at least one of the stored event that ispredicted to occur, the stored action to be performed by the powersource, or a prediction horizon of the prediction for only theparticular time of day or the particular location when the predeterminedquantity of mis-predictions occur during the particular time of day orat the particular location.
 18. A system for improving vehicleefficiency of a vehicle comprising: a power source configured togenerate power usable to propel the vehicle; a sensor configured todetect data corresponding to an environment of the sensor; a memoryconfigured to store a trigger, an event that is predicted to occur whenthe trigger occurs, and an action to take in response to the triggerbeing detected; and an electronic control unit (ECU) coupled to thepower source, the sensor, and the memory and configured to: make aprediction that the stored event will occur in response to the triggeroccurring and the detected data indicating that the stored event islikely to occur, control the power source to take the action when thestored event is predicted to occur, determine a misprediction inresponse to the prediction being inaccurate, and adjust a present orfuture control of the power source in response to at least one of asufficient quantity of mispredictions occurring or a difference betweenthe stored event and an actual event being greater than a predetermineprediction threshold.
 19. The system of claim 18 wherein the ECU isfurther configured to determine the misprediction by determining thatthe actual event fails to fit within a set of bounds defining a correctprediction of the stored event.
 20. The system of claim 18 furthercomprising: a battery having a state of charge (SOC) and configured tostore electrical energy; a motor coupled to the battery and configuredto convert the electrical energy into mechanical power; and an engineconfigured to convert fuel into mechanical power and having an optimaloperating torque and engine speed corresponding to an optimal efficiencyof the engine, wherein the stored event is an acceleration rate, and theaction is to cause the engine to operate at the optimal operating torqueand engine speed prior to the acceleration rate being requested in orderto increase the SOC of the battery so that the motor and the engine canprovide sufficient mechanical power to provide the acceleration ratewithout the engine operating outside of the optimal operating torque andengine speed.